Exploratory Landscape Analysis for Mixed-Variable Problems
R.P. Prager, H. Trautmann, IEEE Transactions on Evolutionary Computation (2024) 1–1.

On the Potential of Normalized TSP Features for Automated Algorithm Selection
J. Heins, J. Bossek, J. Pohl, M. Seiler, H. Trautmann, P. Kerschke, in: Proceedings of the 16th ACM/SIGEVO Conference on Foundations of Genetic Algorithms, Association for Computing Machinery, New York, NY, USA, 2021, pp. 1–15.

Deep Learning as a Competitive Feature-Free Approach for Automated Algorithm Selection on the Traveling Salesperson Problem
M. Seiler, J. Pohl, J. Bossek, P. Kerschke, H. Trautmann, in: Parallel Problem Solving from Nature (PPSN XVI), Springer-Verlag, Berlin, Heidelberg, 2020, pp. 48–64.

Leveraging TSP Solver Complementarity through Machine Learning
P. Kerschke, L. Kotthoff, J. Bossek, H.H. Hoos, H. Trautmann, Evolutionary Computation 26 (2018) 597–620.